##  Family: zero_inflated_poisson 
##   Links: mu = log; zi = logit 
## Formula: n_amr_events ~ ln_livestock_consumption_kg_per_capita + ln_migrant_pop_perc + ln_tourism_inbound_perc + ln_tourism_outbound_perc + ab_export_perc + health_expend_perc + human_consumption_ddd + english_spoken + ln_pubs_sum_per_capita + ln_promed_mentions_per_capita + ln_gdp_per_capita + offset(ln_population) 
##          zi ~ ln_pubs_sum_per_capita + ln_promed_mentions_per_capita + ln_gdp_per_capita + ln_population + english_spoken
##    Data: data[[i]] (Number of observations: 199) 
## Samples: 120 chains, each with iter = 2000; warmup = 1000; thin = 1;
##          total post-warmup samples = 120000
## 
## Population-Level Effects: 
##                                        Estimate Est.Error l-95% CI
## Intercept                                -15.30      3.49   -21.39
## zi_Intercept                              25.10      6.34    13.16
## ln_livestock_consumption_kg_per_capita    -0.40      0.17    -0.67
## ln_migrant_pop_perc                        0.22      0.07     0.09
## ln_tourism_inbound_perc                    0.16      0.14    -0.12
## ln_tourism_outbound_perc                   0.06      0.21    -0.26
## ab_export_perc                             4.80      1.60     0.89
## health_expend_perc                         0.01      0.04    -0.07
## human_consumption_ddd                      0.11      0.02     0.07
## english_spoken                            -0.16      0.17    -0.46
## ln_pubs_sum_per_capita                     0.09      0.14    -0.17
## ln_promed_mentions_per_capita              0.18      0.12    -0.05
## ln_gdp_per_capita                         -0.04      0.14    -0.34
## zi_ln_pubs_sum_per_capita                 -0.09      0.29    -0.68
## zi_ln_promed_mentions_per_capita          -0.02      0.33    -0.66
## zi_ln_gdp_per_capita                      -1.13      0.28    -1.70
## zi_ln_population                          -0.99      0.22    -1.44
## zi_english_spoken                         -0.45      0.48    -1.39
##                                        u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept                                 -7.98 2.36      148      238
## zi_Intercept                              38.02 1.01     5413    46319
## ln_livestock_consumption_kg_per_capita    -0.04 3.97      129      171
## ln_migrant_pop_perc                        0.37 2.31      149      215
## ln_tourism_inbound_perc                    0.44 3.24      134      166
## ln_tourism_outbound_perc                   0.59 3.51      132      149
## ab_export_perc                             7.56 1.87      170      181
## health_expend_perc                         0.10 2.73      140      183
## human_consumption_ddd                      0.16 2.70      140      357
## english_spoken                             0.15 2.27      150      344
## ln_pubs_sum_per_capita                     0.42 1.93      166      214
## ln_promed_mentions_per_capita              0.39 2.40      146      348
## ln_gdp_per_capita                          0.23 2.53      144      206
## zi_ln_pubs_sum_per_capita                  0.47 1.05     1386     3755
## zi_ln_promed_mentions_per_capita           0.62 1.01    19713    88237
## zi_ln_gdp_per_capita                      -0.61 1.03     2669     8451
## zi_ln_population                          -0.57 1.03     2532     5981
## zi_english_spoken                          0.47 1.01     8056    64800
## 
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample 
## is a crude measure of effective sample size, and Rhat is the potential 
## scale reduction factor on split chains (at convergence, Rhat = 1).

## [1] TRUE